Service providers (e.g., wireless and cellular services) and device manufacturers are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services and advancing the underlying technologies. One of interest has been the development of services and technologies for characterizing user behavior with respect to the user's interactions with a device (e.g., a cell phone, smartphone, or other mobile device). More specifically, characterizing user behavior relies, for instance, on correlating user interactions at the device (e.g., making a phone call, accessing an application, etc.) with a context associated with the user or device (e.g., a location, time, date, activity, etc.). However, service providers and device manufacturers face significant technical challenges in making such a correlation because of the differences in the relative sampling frequencies and availability of data between the user interaction data and context data. For example, user interaction data is often collected as discrete data points (e.g., time of phone call) whereas context data is generally collected as a stream of data collected over a period of time at certain frequency.